Specification of human regional brain lineages using orthogonal gradients of WNT and SHH in organoids
Scuderi, S.; Kang, T.-Y.; Jourdon, A.; Yang, L.; Wu, F.; Nelson, A.; Anderson, G. M.; Mariani, J.; SARANGI, V.; Abyzov, A.; Levchenko, A.; Vaccarino, F. M.
Show abstract
The repertory of neurons generated by progenitor cells depends on their location along antero-posterior and dorso-ventral axes of the neural tube. To understand if recreating those axes was sufficient to specify human brain neuronal diversity, we designed a mesofluidic device termed Duo-MAPS to expose induced pluripotent stem cells (iPSC) to concomitant orthogonal gradients of a posteriorizing and a ventralizing morphogen, activating WNT and SHH signaling, respectively. Comparison of single cell transcriptomes with fetal human brain revealed that Duo-MAPS-patterned organoids generated the major neuronal lineages of the forebrain, midbrain, and hindbrain. Morphogens crosstalk translated into early patterns of gene expression programs predicting the generation of specific brain lineages. Human iPSC lines from six different genetic backgrounds showed substantial differences in response to morphogens, suggesting that interindividual genomic and epigenomic variations could impact brain lineages formation. Morphogen gradients promise to be a key approach to model the brain in its entirety.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Specialized signaling centers direct cell fate and spatial organization in a limb organoid model 96%
- Integrated single-cell transcriptomic and epigenetic analyses of cell-state transition and lineage commitment in the embryonic mouse cerebellum 96%
- A telencephalon cell type atlas for goldfish reveals diversity in the evolution of spatial structure and cell types 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.